Background

Canonical correlation was developed by Hotelling (1935, 1936). The
application of canonical correlation is discussed by Cooley and Lohnes
(1971), Tatsuoka (1971), and Mardia, Kent, and Bibby (1979). One of
the best theoretical treatments is given by Kshirsagar (1972).

Consider the situation in which you have a set of p X variables and
q Y variables. The CANCORR procedure finds the linear combinations

w1=a1x1 + a2x2 + ... + apxp

v1 = b1y1 + b2y2 + ... ... + bqyq

such that the correlation between the two canonical variables, w1
and v1, is maximized.
This correlation between the two canonical
variables is the first canonical correlation. The coefficients of the
linear combinations are canonical coefficients or canonical weights.
It is customary to normalize the canonical coefficients so that each
canonical variable has a variance of 1.

PROC CANCORR continues by finding a second set of canonical
variables, uncorrelated with the first pair, that produces
the second highest correlation coefficient.
The process of constructing canonical variables continues
until the number of pairs of canonical variables equals
the number of variables in the smaller group.

Each canonical variable is uncorrelated with all the other
canonical variables of either set except for the one
corresponding canonical variable in the opposite set.
The canonical coefficients are not generally orthogonal, however,
so the canonical variables do not represent jointly perpendicular
directions through the space of the original variables.

The first canonical correlation is at least as large as the multiple
correlation between any variable and the opposite set of variables.
It is possible for the first canonical correlation to be very large
while all the multiple correlations for predicting one of the original
variables from the opposite set of canonical variables are small.
Canonical redundancy analysis (Stewart and Love 1968; Cooley and
Lohnes 1971; van den Wollenberg 1977), which is available with the CANCORR
procedure, examines how well the original variables can be predicted
from the canonical variables.

PROC CANCORR can also perform partial canonical correlation, which is
a multivariate generalization of ordinary partial correlation
(Cooley and Lohnes 1971; Timm 1975).
Most commonly used parametric statistical methods, ranging
from t tests to multivariate analysis of covariance, are
special cases of partial canonical correlation.